Modern data acquisition and processing techniques have boosted the amount of data that has to be managed and evaluated in business environments. The introduction of faster and increasingly sophisticated hardware accelerates this development. Hence, the provision of complete and consistent information becomes an increasingly complex task.
In larger enterprises, enterprise resource planning (ERP) systems like SAP R/3 are commonly used to manage business transactions in a standardized manner. Although conventional ERP systems often have to handle millions of business transactions each day, each individual business transaction usually generates only very little amounts of data. Thus, conventional ERP systems are configured as online transaction processing (OLTP) systems. OLTP systems are optimized as regards the well-defined and fast processing of very small amounts of detailed data. OLTP systems are however generally not suited for analytical tasks that involve the ad-hoc analysis of large data amounts, i.e. require online analytical processing (OLAP).
In the past, various concepts have been deployed to tackle such analytical tasks. One of the most promising concepts is OLAP-based data warehousing. Data warehousing solutions focus on gathering information from various information sources and on providing tools for analyzing the gathered data. Taking into account the advantages of data warehousing it is not surprising that this solution has been incorporated in all kinds of computer networks and in particular in computer networks that include ERP functionalities.
It is obvious that the completeness and consistency of the information delivered by data warehousing techniques is strongly dependent on the completeness and consistency of the data provided to the data warehouse by the individual data sources like ERP systems. Problems as regards the completeness of information delivered to data warehousing applications are often encountered in context with electronic procurement (EP) environments.
EP denotes the electronic generation and transfer of procurement-related data sets in a computer network that includes one or more computers on a buyer's side and one or more computers on the side of each supplier. The information generated and transferred in the computer network include data sets relating to purchase orders, goods delivery, invoicing, etc.
In spite of the advantages associated with EP systems as regards the implementation of standardized and controllable purchase mechanisms, it still happens that employees order directly from a supplier via e-mail, telephone or facsimile, skipping the installed and ready-to-operate EP mechanisms. It is clear that any purchase that has not been performed via the EP system will not be included in the data bases of the EP system and can not be considered by data warehousing mechanisms using these EP data bases as information source. Hence, tasks like analyzing the complete spend paid to suppliers (spend analysis), which is the most important basis for strategic sourcing and for contract negotiations, become inaccurate and error-prone.
In has been found that in many cases up to 30% of all purchases are not performed via the EP environment. This means that data warehousing mechanisms can not reliably be applied to analyze procurement transactions, at least if the EP system is used as single source of information.
In an attempt to make data warehousing mechanisms applicable for the analysis of procurement transactions it has been found that information about procurements performed via EP channels as well as procurements performed via other channels is in many cases already available to the data warehousing applications. The reason for this is the fact that procurement-related information is often included as “by-product” in accounting-related data that have been received in the data warehouse from accounting applications of ERP systems. Accounting-related data are generated for each procurement transaction because regardless of the procurement channel invoices and credit memos are generated and corresponding transactional data sets including accounting data will have to be posted by an ERP accounting component. However, since the accounting data comprise the procurement-related data often in an accumulated format, procurement transactions can only be analyzed very coarsely by data warehousing techniques. Moreover, the informational content of the accounting-related data as regards the analysis of procurement transactions and in particular as regards spend analysis is not sufficient.
In order to improve the analysis accuracy, a technical approach as shown in FIG. 1 might be chosen. Conventionally, accounting related data sets are extracted from an OLTP-based accounting component 210 acting as data source to an OLAP-based data warehousing layer 220 for data gathering and analysis. The data warehousing layer 220 may implement a staged data base approach including a data base 230 for storing the accounting-related data sets that have been extracted from the data source 210 and a further data base 240 that is updated by the first data base 230 and contains the accumulated accounting information comprised in the plurality of extracted data sets.
Based on the not yet accumulated information, i.e. the individual accounting-related data sets included in the data base 230, a parallel data base branch 250, 260 could be used for storing and analyzing procurement-related information contained in the accounting related data sets included in the data base 230. The content (data sets) of the data base 230 could be transferred according to a pre-defined strategy to the data base 250. The data base 250 in turn could then update the data base 260 to accumulate procurement-related information for analysis purposes.
Since the informational content included in the accounting-related data sets as regards procurement analysis is usually not sufficient for a detailed procurement analysis, the accounting-related data sets provided by the data source 210 could be enriched with procurement-related information prior to being extracted. However, such an approach would have the drawback that large amounts of data will have to be transferred between the data source 210 and the data warehousing layer 220, although only 10 to 20% of the information included in the enriched accounting-related data sets will eventually be needed for procurement (e.g. spend) analysis. But not only high network traffic would result, additionally storage and performance problems would occur in view of the fact that very often several millions of accounting-related data sets per day will have to be enriched, extracted and transferred from the data base 230 to the data base 250.
There is thus a need for a technical implementation that facilitates data warehousing for procurement-related information. More specifically, there is a need for a technical implementation which makes procurement-related information that has been generated on a transaction level available to data warehousing techniques while keeping the required network and processing resources low.